feat: OG link previews — track image + meta tags for Telegram/WhatsApp
- bincio/render/ogimage.py: generate 400x400 elevation-coloured PNG with Pillow
- bincio/serve/routers/ogimage.py: /activity/{id}/ OG HTML stub for bot UAs;
/og-image/{user}/{id}.png serves pre-generated images with on-demand fallback
- scripts/generate_og_images.py: batch pre-generation, incremental (mtime skip)
- scripts/strava_elevation_audit.py: add source/threshold/MA columns and pct stats
- pyproject.toml: add Pillow>=10 to serve extras
This commit is contained in:
Davide Scaini
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"""Audit elevation accuracy vs Strava.
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Friends add a note with the Strava elevation to their activity descriptions.
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Supported formats (case-insensitive):
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- "strava 1323md+" most common
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- "strava 1323 m d+"
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- "Strava 1625 m d+"
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- "Strava Elevation 1173m"
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- "1038 m d+ Strava" number before the word strava
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- "Strava 207 metri di dislivello"
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Descriptions live in _merged/activities/ (sidecar merge).
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Computed elevation_gain_m is read from activities/ (main file).
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Usage:
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uv run scripts/strava_elevation_audit.py [--data-dir /var/bincio/data] [--out elevation_audit.csv]
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"""
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from __future__ import annotations
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import argparse
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import csv
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import json
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import re
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import sys
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from pathlib import Path
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from bincio.extract.metrics import elevation_params
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# Patterns tried in order; first match wins.
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# Each pattern must have exactly one capturing group for the numeric value.
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_PATTERNS: list[re.Pattern] = [
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# "strava NNN m ..." or "strava NNNmd+"
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re.compile(r'\bstrava\b\s*([0-9][0-9.,]*)\s*m', re.IGNORECASE),
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# "Strava Elevation NNNm" or "Strava ... NNNm" (one word between)
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re.compile(r'\bstrava\b\s+\w+\s+([0-9][0-9.,]*)\s*m', re.IGNORECASE),
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# "NNN m ... strava" (number comes first, up to 20 chars before strava)
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re.compile(r'([0-9][0-9.,]*)\s*m\b.{0,20}?\bstrava\b', re.IGNORECASE),
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# "Strava NNN metri di dislivello" (Italian)
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re.compile(r'\bstrava\b.*?([0-9][0-9.,]*)\s+metr', re.IGNORECASE),
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]
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def _find_strava_elevation(description: str) -> float | None:
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for pat in _PATTERNS:
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m = pat.search(description)
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if m:
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raw = m.group(1).replace(',', '.')
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try:
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return float(raw)
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except ValueError:
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continue
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return None
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def audit(data_dir: Path, out_path: Path) -> list[dict]:
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rows: list[dict] = []
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unmatched: list[tuple[str, str]] = [] # (path, desc) couldn't parse elevation
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for merged_path in sorted(data_dir.glob("*/_merged/activities/*.json")):
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if merged_path.suffix != ".json":
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continue
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if ".timeseries." in merged_path.name or ".geojson" in merged_path.name:
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continue
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try:
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merged = json.loads(merged_path.read_text(encoding="utf-8"))
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except (json.JSONDecodeError, OSError):
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continue
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description = merged.get("description") or ""
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if not description or "strava" not in description.lower():
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continue
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# Skip strava:// athlete-mention links (not elevation notes)
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if re.search(r'strava://', description, re.IGNORECASE):
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continue
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strava_elev = _find_strava_elevation(description)
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if strava_elev is None:
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unmatched.append((str(merged_path), description))
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continue
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# Read computed elevation from main activity file
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main_path = (
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merged_path.parents[3] # data_dir
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/ merged_path.parents[2].name # user
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/ "activities"
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/ merged_path.name
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)
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try:
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main = json.loads(main_path.read_text(encoding="utf-8"))
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except (json.JSONDecodeError, OSError):
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main = merged # fall back to merged values
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our_elev = main.get("elevation_gain_m")
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title = main.get("title") or merged.get("title") or merged_path.stem
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user = merged_path.parents[2].name
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altitude_source = main.get("altitude_source") or "unknown"
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source = main.get("source") or ""
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device = main.get("device") or "unknown"
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ma_window, threshold = elevation_params(altitude_source, source)
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delta = round(our_elev - strava_elev, 1) if our_elev is not None else None
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pct = (
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round((our_elev - strava_elev) / strava_elev * 100, 1)
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if our_elev is not None and strava_elev != 0
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else None
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)
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rows.append({
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"file": merged_path.name,
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"user": user,
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"title": title,
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"device": device,
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"altitude_source": altitude_source,
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"source": source,
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"ma_window_s": ma_window,
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"threshold_m": threshold,
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"our_elevation_m": our_elev,
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"strava_elevation_m": strava_elev,
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"delta_m": delta,
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"delta_pct": pct,
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"description": description[:120].replace("\n", " ").replace("\r", ""),
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})
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rows.sort(key=lambda r: abs(r["delta_m"] or 0), reverse=True)
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if rows:
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with out_path.open("w", newline="", encoding="utf-8") as f:
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writer = csv.DictWriter(f, fieldnames=list(rows[0].keys()))
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writer.writeheader()
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writer.writerows(rows)
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if unmatched:
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print(f"\nCould not parse elevation from {len(unmatched)} description(s):")
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for path, desc in unmatched:
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print(f" {Path(path).name} {desc[:80]!r}")
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return rows
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def main() -> None:
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ap = argparse.ArgumentParser(description="Audit elevation accuracy vs Strava notes")
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ap.add_argument("--data-dir", default="/var/bincio/data", type=Path)
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ap.add_argument("--out", default="elevation_audit.csv", type=Path)
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args = ap.parse_args()
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if not args.data_dir.exists():
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print(f"ERROR: data dir not found: {args.data_dir}", file=sys.stderr)
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sys.exit(1)
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print(f"Scanning {args.data_dir} …")
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rows = audit(args.data_dir, args.out)
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if not rows:
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print("No activities found with a parseable Strava elevation note.")
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return
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print(f"\nFound {len(rows)} activit{'y' if len(rows)==1 else 'ies'}:\n")
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header = (
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f"{'File':<50} {'User':<15} {'Source':<16} {'AltSrc':<12}"
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f" {'MA':>4} {'Thr':>5} {'Ours':>8} {'Strava':>8} {'Delta':>8} {'Delta%':>7}"
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)
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print(header)
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print("-" * len(header))
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for r in rows:
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delta_str = f"{r['delta_m']:+.0f}" if r['delta_m'] is not None else "n/a"
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pct_str = f"{r['delta_pct']:+.1f}%" if r['delta_pct'] is not None else "n/a"
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our_str = f"{r['our_elevation_m']:.0f}" if r['our_elevation_m'] is not None else "n/a"
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print(
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f"{r['file']:<50} {r['user']:<15} {r['source']:<16} {r['altitude_source']:<12}"
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f" {r['ma_window_s']:>4} {r['threshold_m']:>5.1f}"
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f" {our_str:>8} {r['strava_elevation_m']:>8.0f}"
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f" {delta_str:>8} {pct_str:>7}"
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)
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n = len(rows)
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pcts = [r["delta_pct"] for r in rows if r["delta_pct"] is not None]
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deltas = [r["delta_m"] for r in rows if r["delta_m"] is not None]
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if pcts:
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avg_pct = sum(pcts) / len(pcts)
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sorted_pcts = sorted(pcts)
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median_pct = sorted_pcts[len(sorted_pcts) // 2]
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within_10 = sum(1 for p in pcts if abs(p) <= 10)
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within_15 = sum(1 for p in pcts if abs(p) <= 15)
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avg_d = sum(deltas) / len(deltas) if deltas else 0
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print(
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f"\n n={n} avg={avg_pct:+.1f}% median={median_pct:+.1f}%"
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f" avg delta={avg_d:+.0f} m"
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f" within ±10%: {within_10}/{n} within ±15%: {within_15}/{n}"
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)
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print(f"\nCSV saved to: {args.out}")
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if __name__ == "__main__":
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main()
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