owain
ec87f68729
- Grey out trend ranges beyond available health history - Reject implausibly fast (vehicle) activities on upload with feedback - Add cancel button + cooperative cancellation for Garmin sync - Show daily steps prominently on the dashboard - Clear errors for malformed/empty upload ZIPs - Snap-target dot when drawing a segment on the map - Time-axis fallback for stationary/HIIT HR timelines; hide map when no GPS - Parse and display moving time (timer) vs elapsed; backfill task - Restyle SegmentsPanel like RouteLeaderboard; Laps/Routes/Segments on one row Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
388 lines
15 KiB
Python
388 lines
15 KiB
Python
"""
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FIT and GPX file parser.
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Parses FIT files directly using the Garmin SDK but applies manual
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scale conversion for fields where the SDK doesn't auto-convert.
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"""
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import math
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import struct
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from datetime import datetime, timezone
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from typing import Optional
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import gpxpy
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import polyline as polyline_lib
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from garmin_fit_sdk import Decoder, Stream
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FIT_EPOCH_S = 631065600
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SEMICIRCLES_TO_DEG = 180.0 / (2 ** 31)
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def _semicircles_to_deg(val):
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if val is None:
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return None
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try:
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result = float(val) * SEMICIRCLES_TO_DEG
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if -90 <= result <= 90 or -180 <= result <= 180:
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return result
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except (TypeError, ValueError):
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pass
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return None
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def _safe_float(val) -> Optional[float]:
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try:
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return float(val) if val is not None else None
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except (TypeError, ValueError):
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return None
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def _sanitize_speed(val, dist_m=None, dur_s=None) -> Optional[float]:
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"""Reject the FIT invalid sentinel (0xFFFF/1000 = 65.535 m/s) and fall back to dist/dur."""
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fv = _safe_float(val)
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if fv is None or fv >= 65.0:
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if dist_m and dur_s and float(dur_s) > 0:
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return float(dist_m) / float(dur_s)
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return None
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return fv
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# Conservative average-speed ceilings (m/s) above which an activity was almost
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# certainly recorded in a vehicle rather than under human power. Sports not
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# listed fall back to the generous default.
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_VEHICLE_SPEED_CEILINGS = {
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"running": 8.0, # ~28.8 km/h — well above elite sprint pace sustained
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"walking": 8.0,
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"hiking": 8.0,
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"cycling": 22.0, # ~79 km/h — beyond sustained amateur cycling
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}
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_VEHICLE_SPEED_DEFAULT = 25.0 # ~90 km/h
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def _vehicle_reason(sport_type, avg_speed_ms, dist_m=None, dur_s=None) -> Optional[str]:
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"""Return a human-readable reason if the average speed is implausibly fast for
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the sport (i.e. the 'activity' looks like car/vehicle travel), else None."""
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speed = _safe_float(avg_speed_ms)
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if speed is None and dist_m and dur_s and float(dur_s) > 0:
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speed = float(dist_m) / float(dur_s)
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if speed is None or speed <= 0:
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return None
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ceiling = _VEHICLE_SPEED_CEILINGS.get(sport_type, _VEHICLE_SPEED_DEFAULT)
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if speed > ceiling:
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return (f"Looks like vehicle travel — average speed {speed * 3.6:.0f} km/h "
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f"exceeds the plausible limit for {sport_type}")
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return None
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def _bounding_box(coords):
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if not coords:
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return None
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lats = [c[0] for c in coords]
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lons = [c[1] for c in coords]
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return {"min_lat": min(lats), "max_lat": max(lats),
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"min_lon": min(lons), "max_lon": max(lons)}
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def _to_dt(val) -> Optional[datetime]:
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if val is None:
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return None
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if isinstance(val, datetime):
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return val.replace(tzinfo=timezone.utc) if val.tzinfo is None else val
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if isinstance(val, (int, float)):
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try:
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return datetime.fromtimestamp(int(val) + FIT_EPOCH_S, tz=timezone.utc)
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except (OSError, OverflowError, ValueError):
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return None
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return None
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def _is_valid_lat(v):
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return v is not None and -90 <= v <= 90
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def _is_valid_lon(v):
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return v is not None and -180 <= v <= 180
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def parse_fit_file(filepath: str) -> dict:
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session_data = {}
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records = []
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laps = []
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def listener(mesg_num: int, msg: dict):
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if mesg_num == 18: # session
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session_data.update(msg)
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elif mesg_num == 20: # record
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records.append(msg)
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elif mesg_num == 19: # lap
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laps.append(msg)
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stream = Stream.from_file(filepath)
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decoder = Decoder(stream)
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decoder.read(
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apply_scale_and_offset=True,
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convert_datetimes_to_dates=True,
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convert_types_to_strings=True,
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enable_crc_check=False,
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expand_sub_fields=True,
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expand_components=True,
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merge_heart_rates=True,
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mesg_listener=listener,
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)
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# The SDK may return field names in camelCase or snake_case depending on version.
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# Try both. Also handle raw timestamp integers for start_time.
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def get(d, *keys):
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for k in keys:
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v = d.get(k)
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if v is not None:
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return v
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return None
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sport_raw = str(get(session_data, "sport", "Sport") or "generic").lower()
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sport_map = {
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"running": "running", "cycling": "cycling",
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"hiking": "hiking", "walking": "walking",
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"generic": "other", "trail_running": "running",
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"e_biking": "cycling", "open_water_swimming": "other",
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}
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sport_type = sport_map.get(sport_raw, sport_raw)
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# start_time — SDK may return datetime or raw int
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start_time_raw = get(session_data, "startTime", "start_time")
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start_time = _to_dt(start_time_raw)
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# Position fields — the SDK may or may not convert semicircles.
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# Check if values look like semicircles (>= 90 for lat) and convert if so.
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def get_lat(d):
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v = get(d, "positionLat", "position_lat")
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if v is None:
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return None
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fv = _safe_float(v)
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if fv is None:
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return None
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# If absolute value > 90, it's semicircles
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if abs(fv) > 90:
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fv = fv * SEMICIRCLES_TO_DEG
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return fv if _is_valid_lat(fv) else None
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def get_lon(d):
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v = get(d, "positionLong", "position_long")
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if v is None:
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return None
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fv = _safe_float(v)
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if fv is None:
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return None
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if abs(fv) > 180:
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fv = fv * SEMICIRCLES_TO_DEG
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return fv if _is_valid_lon(fv) else None
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# Build GPS track
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coords = []
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for r in records:
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lat = get_lat(r)
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lon = get_lon(r)
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if lat is not None and lon is not None:
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coords.append((lat, lon))
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encoded_polyline = polyline_lib.encode(coords) if coords else None
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bounding_box = _bounding_box(coords)
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# Normalize data points
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normalized_points = []
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for r in records:
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ts = _to_dt(get(r, "timestamp"))
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lat = get_lat(r)
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lon = get_lon(r)
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altitude = get(r, "altitude", "enhancedAltitude", "enhanced_altitude")
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hr = get(r, "heartRate", "heart_rate")
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cadence = get(r, "cadence")
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speed = get(r, "speed", "enhancedSpeed", "enhanced_speed")
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power = get(r, "power")
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temp = get(r, "temperature")
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distance = get(r, "distance")
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normalized_points.append({
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"timestamp": ts.isoformat() if ts else None,
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"latitude": _safe_float(lat),
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"longitude": _safe_float(lon),
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"altitude_m": _safe_float(altitude),
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"heart_rate": _safe_float(hr),
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"cadence": _safe_float(cadence),
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"speed_ms": _safe_float(speed),
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"power": _safe_float(power),
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"temperature_c": _safe_float(temp),
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"distance_m": _safe_float(distance),
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})
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# Normalize laps
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normalized_laps = []
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for i, lap in enumerate(laps):
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ls = _to_dt(get(lap, "startTime", "start_time"))
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lap_dist = _safe_float(get(lap, "totalDistance", "total_distance"))
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lap_dur = _safe_float(get(lap, "totalElapsedTime", "total_elapsed_time"))
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normalized_laps.append({
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"lap_number": i + 1,
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"start_time": ls.isoformat() if ls else None,
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"duration_s": lap_dur,
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"distance_m": lap_dist,
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"avg_heart_rate": _safe_float(get(lap, "avgHeartRate", "avg_heart_rate")),
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"avg_cadence": _safe_float(get(lap, "avgCadence", "avg_cadence")),
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"avg_speed_ms": _sanitize_speed(
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get(lap, "avgSpeed", "avg_speed", "enhancedAvgSpeed", "enhanced_avg_speed"),
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dist_m=lap_dist, dur_s=lap_dur,
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),
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"avg_power": _safe_float(get(lap, "avgPower", "avg_power")),
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})
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name = sport_type.title()
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if start_time:
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name += " " + start_time.strftime("%Y-%m-%d")
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total_dist = _safe_float(get(session_data, "totalDistance", "total_distance"))
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elapsed_s = _safe_float(get(session_data, "totalElapsedTime", "total_elapsed_time"))
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# Timer time = time the device was actively recording (excludes auto/manual pauses).
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moving_s = _safe_float(get(session_data, "totalTimerTime", "total_timer_time"))
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avg_speed = _sanitize_speed(
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get(session_data, "avgSpeed", "avg_speed", "enhancedAvgSpeed", "enhanced_avg_speed"),
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dist_m=total_dist, dur_s=elapsed_s,
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)
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return {
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"name": name,
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"sport_type": sport_type,
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"start_time": start_time.isoformat() if start_time else None,
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"distance_m": total_dist,
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"duration_s": elapsed_s,
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"moving_time_s": moving_s,
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"elevation_gain_m": _safe_float(get(session_data, "totalAscent", "total_ascent")),
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"elevation_loss_m": _safe_float(get(session_data, "totalDescent", "total_descent")),
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"avg_heart_rate": _safe_float(get(session_data, "avgHeartRate", "avg_heart_rate")),
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"max_heart_rate": _safe_float(get(session_data, "maxHeartRate", "max_heart_rate")),
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"avg_cadence": _safe_float(get(session_data, "avgCadence", "avg_cadence")),
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"avg_power": _safe_float(get(session_data, "avgPower", "avg_power")),
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"normalized_power": _safe_float(get(session_data, "normalizedPower", "normalized_power")),
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"avg_speed_ms": avg_speed,
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"max_speed_ms": _safe_float(get(session_data, "maxSpeed", "max_speed",
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"enhancedMaxSpeed", "enhanced_max_speed")),
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"avg_temperature_c": _safe_float(get(session_data, "avgTemperature", "avg_temperature")),
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"calories": _safe_float(get(session_data, "totalCalories", "total_calories")),
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"training_stress_score": _safe_float(get(session_data, "trainingStressScore",
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"training_stress_score")),
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"vo2max_estimate": _safe_float(get(session_data, "totalTrainingEffect",
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"total_training_effect")),
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"polyline": encoded_polyline,
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"bounding_box": bounding_box,
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"source_type": "fit",
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"rejected_reason": _vehicle_reason(sport_type, avg_speed, total_dist, moving_s or elapsed_s),
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"data_points": normalized_points,
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"laps": normalized_laps,
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}
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def parse_gpx_file(filepath: str) -> dict:
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with open(filepath) as f:
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gpx = gpxpy.parse(f)
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data_points = []
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track = gpx.tracks[0] if gpx.tracks else None
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if not track:
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raise ValueError("No tracks found in GPX file")
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for segment in track.segments:
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for pt in segment.points:
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ts = pt.time
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if ts and ts.tzinfo is None:
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ts = ts.replace(tzinfo=timezone.utc)
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extensions = {}
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if pt.extensions:
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for ext in pt.extensions:
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for child in ext:
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tag = child.tag.split("}")[-1] if "}" in child.tag else child.tag
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try:
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extensions[tag] = float(child.text)
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except (ValueError, TypeError):
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pass
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data_points.append({
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"timestamp": ts.isoformat() if ts else None,
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"latitude": pt.latitude, "longitude": pt.longitude,
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"altitude_m": pt.elevation,
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"heart_rate": extensions.get("hr"),
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"cadence": extensions.get("cad"),
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"speed_ms": extensions.get("speed"),
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"power": extensions.get("power"),
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"temperature_c": extensions.get("temp") or extensions.get("atemp"),
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"distance_m": None,
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})
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coords = [(p["latitude"], p["longitude"]) for p in data_points if p["latitude"] and p["longitude"]]
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encoded_polyline = polyline_lib.encode(coords) if coords else None
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bounding_box = _bounding_box(coords)
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total_dist = 0.0
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prev = None
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for p in data_points:
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if p["latitude"] and p["longitude"]:
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if prev:
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R = 6371000
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phi1, phi2 = math.radians(prev[0]), math.radians(p["latitude"])
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dphi = math.radians(p["latitude"] - prev[0])
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dlam = math.radians(p["longitude"] - prev[1])
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a = math.sin(dphi/2)**2 + math.cos(phi1)*math.cos(phi2)*math.sin(dlam/2)**2
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total_dist += 2 * R * math.asin(math.sqrt(a))
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prev = (p["latitude"], p["longitude"])
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p["distance_m"] = total_dist
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uphill, downhill = 0.0, 0.0
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alts = [p["altitude_m"] for p in data_points if p["altitude_m"]]
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for i in range(1, len(alts)):
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diff = alts[i] - alts[i-1]
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if diff > 0: uphill += diff
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else: downhill += abs(diff)
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hrs = [p["heart_rate"] for p in data_points if p["heart_rate"]]
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start_time_str = data_points[0]["timestamp"] if data_points else None
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start_dt = datetime.fromisoformat(start_time_str) if start_time_str else None
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end_dt = datetime.fromisoformat(data_points[-1]["timestamp"]) if data_points else None
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duration = (end_dt - start_dt).total_seconds() if (start_dt and end_dt) else None
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sport = track.type.lower() if track.type else "running"
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gpx_avg_speed = (total_dist / duration) if (total_dist and duration) else None
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return {
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"name": track.name or gpx.name or f"Activity {start_dt.date() if start_dt else ''}",
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"sport_type": sport, "start_time": start_time_str,
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"distance_m": total_dist, "duration_s": duration, "moving_time_s": None,
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"elevation_gain_m": uphill, "elevation_loss_m": downhill,
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"avg_heart_rate": (sum(hrs) / len(hrs)) if hrs else None,
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"max_heart_rate": max(hrs) if hrs else None,
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"avg_cadence": None, "avg_power": None, "normalized_power": None,
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"avg_speed_ms": gpx_avg_speed,
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"max_speed_ms": None, "avg_temperature_c": None, "calories": None,
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"training_stress_score": None, "vo2max_estimate": None,
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"polyline": encoded_polyline, "bounding_box": bounding_box,
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"source_type": "gpx",
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"rejected_reason": _vehicle_reason(sport, gpx_avg_speed, total_dist, duration),
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"data_points": data_points, "laps": [],
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}
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def calculate_hr_zones(data_points: list, user_max_hr: float) -> dict:
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if not user_max_hr or user_max_hr < 100:
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return {}
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zone_bounds = [0.0, 0.60, 0.70, 0.80, 0.90, 1.01]
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zone_keys = ["z1", "z2", "z3", "z4", "z5"]
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zones = {k: 0 for k in zone_keys}
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total = 0
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for p in data_points:
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hr = p.get("heart_rate")
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if not hr or hr < 20:
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continue
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pct = hr / user_max_hr
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total += 1
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for i, key in enumerate(zone_keys):
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if zone_bounds[i] <= pct < zone_bounds[i+1]:
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zones[key] += 1
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break
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else:
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zones["z5"] += 1
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if total:
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return {k: round(v / total * 100, 1) for k, v in zones.items()}
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return {} |