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
@@ -20,6 +20,7 @@ from bincio.serve.cli import serve # noqa: E402
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from bincio.dev import dev # noqa: E402
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from bincio.reextract_cmd import reextract_originals # noqa: E402
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from bincio.sync_strava import sync_strava_cmd # noqa: E402
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from bincio.segments.cli import segments_group # noqa: E402
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main.add_command(extract)
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main.add_command(render)
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@@ -30,3 +31,4 @@ main.add_command(serve)
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main.add_command(dev)
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main.add_command(reextract_originals)
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main.add_command(sync_strava_cmd)
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main.add_command(segments_group)
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@@ -74,6 +74,15 @@ def ingest_parsed(
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pass
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write_athlete_json(list(summaries.values()), data_dir, athlete_config)
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# Detect segment efforts for this activity (non-fatal if it fails).
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try:
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from bincio.segments.detect import track_from_parsed, detect_all
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track = track_from_parsed(parsed, activity_id)
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if track is not None:
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detect_all(track, data_dir.name, data_dir.parent)
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except Exception:
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pass
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return activity_id
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@@ -0,0 +1,114 @@
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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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@@ -2484,6 +2484,87 @@ async def delete_segment(
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return JSONResponse({"ok": True})
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@app.get("/api/segments/{segment_id}/efforts")
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async def get_segment_efforts(
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segment_id: str,
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bincio_session: Optional[str] = Cookie(default=None),
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) -> JSONResponse:
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"""Return all efforts on a segment for the logged-in user."""
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user = _require_user(bincio_session)
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dd = _get_data_dir()
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seg = _seg_store.load_segment(dd, segment_id)
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if seg is None:
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raise HTTPException(404, "Segment not found")
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efforts = _seg_store.load_efforts(dd, user.handle, segment_id)
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return JSONResponse([
|
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{
|
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"activity_id": e.activity_id,
|
||||
"started_at": _seg_store._iso(e.started_at),
|
||||
"elapsed_s": e.elapsed_s,
|
||||
"avg_speed_kmh": e.avg_speed_kmh,
|
||||
"avg_hr_bpm": e.avg_hr_bpm,
|
||||
"avg_power_w": e.avg_power_w,
|
||||
"np_power_w": e.np_power_w,
|
||||
}
|
||||
for e in efforts
|
||||
])
|
||||
|
||||
|
||||
@app.post("/api/segments/{segment_id}/detect")
|
||||
async def trigger_detect(
|
||||
segment_id: str,
|
||||
bincio_session: Optional[str] = Cookie(default=None),
|
||||
) -> JSONResponse:
|
||||
"""Retroactively detect efforts on a segment for the logged-in user."""
|
||||
user = _require_user(bincio_session)
|
||||
dd = _get_data_dir()
|
||||
seg = _seg_store.load_segment(dd, segment_id)
|
||||
if seg is None:
|
||||
raise HTTPException(404, "Segment not found")
|
||||
|
||||
from datetime import datetime as _datetime
|
||||
from bincio.segments.detect import track_from_timeseries_json, detect_one
|
||||
import json as _json
|
||||
|
||||
user_dir = dd / user.handle
|
||||
acts_dir = user_dir / "activities"
|
||||
total = 0
|
||||
for detail_path in sorted(acts_dir.glob("*.json")):
|
||||
if ".timeseries." in detail_path.name:
|
||||
continue
|
||||
try:
|
||||
detail = _json.loads(detail_path.read_text(encoding="utf-8"))
|
||||
except Exception:
|
||||
continue
|
||||
ts_url = detail.get("timeseries_url")
|
||||
if not ts_url:
|
||||
continue
|
||||
ts_path = user_dir / ts_url
|
||||
if not ts_path.exists():
|
||||
continue
|
||||
try:
|
||||
ts = _json.loads(ts_path.read_text(encoding="utf-8"))
|
||||
except Exception:
|
||||
continue
|
||||
started_raw = detail.get("started_at")
|
||||
if not started_raw:
|
||||
continue
|
||||
try:
|
||||
started_at = _datetime.fromisoformat(started_raw.replace("Z", "+00:00"))
|
||||
except Exception:
|
||||
continue
|
||||
track = track_from_timeseries_json(ts, detail.get("id", detail_path.stem),
|
||||
detail.get("sport", "other"), started_at)
|
||||
if track is None:
|
||||
continue
|
||||
efforts = detect_one(track, seg)
|
||||
for effort in efforts:
|
||||
_seg_store.add_effort(dd, user.handle, segment_id, effort)
|
||||
total += len(efforts)
|
||||
|
||||
return JSONResponse({"ok": True, "efforts_found": total})
|
||||
|
||||
|
||||
# ── Feedback ──────────────────────────────────────────────────────────────────
|
||||
|
||||
_FEEDBACK_IMAGE_SUFFIXES = {".jpg", ".jpeg", ".png", ".gif", ".webp", ".heic"}
|
||||
|
||||
@@ -0,0 +1,189 @@
|
||||
# Bincio Segments — Design Plan
|
||||
|
||||
## Overview
|
||||
|
||||
Segments are named GPS stretches that can be defined by any user. Every time an activity passes through a segment, an "effort" is recorded automatically. Users can track their personal history and PRs on each segment.
|
||||
|
||||
---
|
||||
|
||||
## Data Model
|
||||
|
||||
### Segment (global, shared)
|
||||
|
||||
File: `/var/bincio/segments/{segment-id}.json`
|
||||
|
||||
```json
|
||||
{
|
||||
"id": "climb-to-passo-rolle-a3f2",
|
||||
"name": "Climb to Passo Rolle",
|
||||
"sport": "cycling",
|
||||
"polyline": [[46.123, 11.456], [46.124, 11.457], "..."],
|
||||
"distance_m": 4320.5,
|
||||
"bbox": [11.456, 46.120, 11.502, 46.135],
|
||||
"created_by": "brut",
|
||||
"created_at": "2026-05-13T10:00:00Z"
|
||||
}
|
||||
```
|
||||
|
||||
Segment ID: slugified name + 4-char hash to avoid collisions.
|
||||
|
||||
### SegmentEffort (per user)
|
||||
|
||||
File: `/var/bincio/data/{handle}/segment_efforts/{segment-id}.json`
|
||||
|
||||
Array of efforts, newest first:
|
||||
|
||||
```json
|
||||
[
|
||||
{
|
||||
"activity_id": "2026-04-26T053013Z-ccbas-...",
|
||||
"started_at": "2026-04-26T06:12:33Z",
|
||||
"elapsed_s": 1842,
|
||||
"avg_speed_kmh": 12.4,
|
||||
"avg_hr_bpm": 158,
|
||||
"avg_power_w": 245,
|
||||
"np_power_w": 261,
|
||||
"detected_at": "2026-05-13T10:05:00Z"
|
||||
}
|
||||
]
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## Detection Algorithm
|
||||
|
||||
**Input:** activity GPS track (lat/lon, ~1 Hz), segment polyline
|
||||
|
||||
**Steps:**
|
||||
|
||||
1. **Bbox pre-filter** — skip if activity bbox doesn't overlap segment bbox (padded by ~50 m). O(1) per segment.
|
||||
|
||||
2. **Start proximity** — find all activity points within 25 m of `polyline[0]`. Each is a candidate entry point.
|
||||
|
||||
3. **End proximity** — from each candidate entry, scan forward (never backward) for an activity point within 25 m of `polyline[-1]`. Enforces correct direction.
|
||||
|
||||
4. **Path conformance** — between entry and exit indices, project each activity point onto the nearest segment polyline segment and compute perpendicular distance. If more than 30% of points exceed 50 m deviation, reject the match. Configurable thresholds.
|
||||
|
||||
5. **Effort extraction** — from the matched entry/exit indices, slice the timeseries and compute `elapsed_s` and avg metrics.
|
||||
|
||||
6. **Multiple efforts** — after a successful match, continue scanning from the exit point to detect repeat efforts in the same activity (e.g. repeat climbs).
|
||||
|
||||
**Configurable constants** (easy to tune):
|
||||
- `MATCH_RADIUS_M = 25` — proximity to start/end points
|
||||
- `CONFORMANCE_MAX_DEVIATION_M = 50` — max perpendicular distance
|
||||
- `CONFORMANCE_MAX_FRACTION = 0.30` — fraction of points allowed to exceed max deviation
|
||||
|
||||
---
|
||||
|
||||
## Server-side Code Structure
|
||||
|
||||
```
|
||||
bincio/segments/
|
||||
__init__.py
|
||||
models.py — Segment, SegmentEffort dataclasses
|
||||
store.py — read/write segments and efforts to/from /var/bincio
|
||||
detect.py — matching algorithm
|
||||
cli.py — bincio segments detect [--activity X] [--segment Y] [--user U] [--all]
|
||||
```
|
||||
|
||||
Detection is called:
|
||||
- **At ingest time** — after each new activity is processed, reads all segment definitions (bbox pre-filter makes this fast), writes matches to the user's `segment_efforts/`
|
||||
- **Retroactively** — via CLI or API endpoint, for one user re-running all activities against all (or one) segment(s)
|
||||
|
||||
---
|
||||
|
||||
## API Endpoints
|
||||
|
||||
All new, added to `server.py`:
|
||||
|
||||
```
|
||||
GET /api/segments?bbox=lon_min,lat_min,lon_max,lat_max list segments in viewport
|
||||
POST /api/segments create segment (auth required)
|
||||
DELETE /api/segments/{id} delete (created_by or admin only)
|
||||
|
||||
GET /api/segments/{id}/efforts current user's efforts (auth)
|
||||
POST /api/segments/{id}/detect retrigger detection for current user
|
||||
```
|
||||
|
||||
Bbox filtering is done in memory at request time. All segment definitions are read from disk on startup (or lazily cached). Works fine for hundreds of segments.
|
||||
|
||||
---
|
||||
|
||||
## Frontend
|
||||
|
||||
### New pages / routes
|
||||
|
||||
| Route | Purpose |
|
||||
|---|---|
|
||||
| `/segments/` | Map + list of all segments; entry point for browsing and creating |
|
||||
| `/segments/new/` | Segment creation flow |
|
||||
| `/segments/{id}/` | Segment detail: polyline on map + user's effort history |
|
||||
|
||||
### `/segments/` page
|
||||
|
||||
- Full-width map showing segment polylines for segments whose bbox overlaps the current map viewport
|
||||
- Segment list below (or sidebar): name, sport, distance, creator
|
||||
- "New segment" button → `/segments/new/`
|
||||
- Clicking a segment → `/segments/{id}/`
|
||||
|
||||
### `/segments/new/` — creation flow
|
||||
|
||||
1. Searchable list of the user's own activities with GPS → select one
|
||||
2. Activity track shown on map
|
||||
3. Dual-handle range slider below the map, one position per GPS point in the track
|
||||
- Moving handles highlights the selected portion on the map in a contrasting colour
|
||||
- Start and end coordinates are the GPS points at the slider handle positions
|
||||
4. Name input + optional sport selector
|
||||
5. Confirm → `POST /api/segments`
|
||||
|
||||
### `/segments/{id}/` — segment detail
|
||||
|
||||
- Segment polyline on map
|
||||
- Metadata: name, sport, distance, created by
|
||||
- Effort history table (sorted by date, newest first):
|
||||
- Date, elapsed time, avg speed, avg HR, avg power, NP
|
||||
- Delta vs. personal best highlighted
|
||||
- "Retrigger detection" button → `POST /api/segments/{id}/detect`
|
||||
|
||||
### Activity detail page (existing)
|
||||
|
||||
Below the stats table: if any segment efforts were detected for this activity, show a compact list — segment name + elapsed time + Δ vs. PR.
|
||||
|
||||
### Athlete page (existing)
|
||||
|
||||
New "Segments" section: list of segments the user has efforts on, showing best time and effort count for each.
|
||||
|
||||
---
|
||||
|
||||
## Segment Creation UX (refined)
|
||||
|
||||
Standalone `/segments/new/` page with an activity picker. Additionally, the activity detail page gets a **"Create segment from this activity"** button that links to `/segments/new/?activity={id}`, pre-selecting the activity and skipping the picker step.
|
||||
|
||||
Rationale: users will typically think "I want to turn this climb I just did into a segment" — the shortcut from activity detail makes that fast, while the standalone page remains the canonical entry point for discovery.
|
||||
|
||||
---
|
||||
|
||||
## Implementation Strategy
|
||||
|
||||
UI-first, so users can start populating the segment collection immediately. Detection comes after.
|
||||
|
||||
**Phase 1 — Minimal backend to unblock UI** ✅
|
||||
1. `bincio/segments/models.py` — Segment and SegmentEffort dataclasses ✅
|
||||
2. `bincio/segments/store.py` — read/write segments to/from disk ✅
|
||||
3. `POST /api/segments` + `GET /api/segments?bbox=...` + `DELETE /api/segments/{id}` ✅
|
||||
|
||||
**Phase 2 — Creation UI** ✅
|
||||
4. `/segments/new/` page — activity picker → map + dual-handle slider → name/save ✅
|
||||
5. `/segments/` page — map + list of existing segments, bbox-filtered ✅
|
||||
6. Activity detail — "Create segment from this activity" shortcut button ✅
|
||||
|
||||
**Phase 3 — Detection** ✅
|
||||
7. `bincio/segments/detect.py` — matching algorithm, tested in isolation ✅
|
||||
8. `bincio/segments/cli.py` — retroactive detection CLI (`bincio segments detect`) ✅
|
||||
9. Ingest hook — run detection at end of each ingest ✅
|
||||
10. `POST /api/segments/{id}/detect` + `GET /api/segments/{id}/efforts` ✅
|
||||
|
||||
**Phase 4 — Display** (next)
|
||||
11. `/segments/{id}/` page — segment detail + effort history table
|
||||
12. Activity detail — show matched segment efforts below stats
|
||||
13. Athlete page — segments section
|
||||
Reference in New Issue
Block a user