284 lines
10 KiB
Python
284 lines
10 KiB
Python
"""
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FIT and GPX file parser using the official Garmin FIT Python SDK.
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Field names from the SDK are camelCase as per the SDK documentation.
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"""
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import math
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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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def haversine_distance(lat1, lon1, lat2, lon2) -> float:
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R = 6371000
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phi1, phi2 = math.radians(lat1), math.radians(lat2)
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dphi = math.radians(lat2 - lat1)
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dlam = math.radians(lon2 - lon1)
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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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return 2 * R * math.asin(math.sqrt(a))
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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 _bounding_box(coords: list) -> Optional[dict]:
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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 _ensure_utc(dt) -> Optional[datetime]:
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if dt is None:
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return None
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if isinstance(dt, datetime):
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if dt.tzinfo is None:
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return dt.replace(tzinfo=timezone.utc)
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return dt
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return None
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def parse_fit_file(filepath: str) -> dict:
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"""Parse a Garmin .fit activity file using the official Garmin SDK."""
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stream = Stream.from_file(filepath)
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decoder = Decoder(stream)
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messages, errors = 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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)
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# SDK returns camelCase keys
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sessions = messages.get("session", [{}])
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session = sessions[0] if sessions else {}
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records = messages.get("record", [])
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laps = messages.get("lap", [])
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sport = str(session.get("sport", "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, sport)
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start_time = _ensure_utc(session.get("startTime"))
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coords = []
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for r in records:
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lat = r.get("positionLat")
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lon = r.get("positionLong")
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if lat is not None and lon is not None:
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if -90 <= lat <= 90 and -180 <= lon <= 180:
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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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normalized_points = []
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for r in records:
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ts = _ensure_utc(r.get("timestamp"))
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lat = r.get("positionLat")
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lon = r.get("positionLong")
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if lat is not None and not (-90 <= lat <= 90):
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lat = None
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if lon is not None and not (-180 <= lon <= 180):
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lon = None
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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(r.get("altitude") or r.get("enhancedAltitude")),
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"heart_rate": _safe_float(r.get("heartRate")),
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"cadence": _safe_float(r.get("cadence")),
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"speed_ms": _safe_float(r.get("speed") or r.get("enhancedSpeed")),
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"power": _safe_float(r.get("power")),
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"temperature_c": _safe_float(r.get("temperature")),
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"distance_m": _safe_float(r.get("distance")),
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})
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normalized_laps = []
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for i, lap in enumerate(laps):
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ls = _ensure_utc(lap.get("startTime"))
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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": _safe_float(lap.get("totalElapsedTime")),
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"distance_m": _safe_float(lap.get("totalDistance")),
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"avg_heart_rate": _safe_float(lap.get("avgHeartRate")),
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"avg_cadence": _safe_float(lap.get("avgCadence")),
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"avg_speed_ms": _safe_float(lap.get("avgSpeed") or lap.get("enhancedAvgSpeed")),
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"avg_power": _safe_float(lap.get("avgPower")),
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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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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": _safe_float(session.get("totalDistance")),
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"duration_s": _safe_float(session.get("totalElapsedTime")),
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"elevation_gain_m": _safe_float(session.get("totalAscent")),
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"elevation_loss_m": _safe_float(session.get("totalDescent")),
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"avg_heart_rate": _safe_float(session.get("avgHeartRate")),
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"max_heart_rate": _safe_float(session.get("maxHeartRate")),
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"avg_cadence": _safe_float(session.get("avgCadence")),
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"avg_power": _safe_float(session.get("avgPower")),
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"normalized_power": _safe_float(session.get("normalizedPower")),
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"avg_speed_ms": _safe_float(session.get("avgSpeed") or session.get("enhancedAvgSpeed")),
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"max_speed_ms": _safe_float(session.get("maxSpeed") or session.get("enhancedMaxSpeed")),
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"avg_temperature_c": _safe_float(session.get("avgTemperature")),
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"calories": _safe_float(session.get("totalCalories")),
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"training_stress_score": _safe_float(session.get("trainingStressScore")),
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"vo2max_estimate": _safe_float(session.get("totalTrainingEffect")),
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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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"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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"""Parse a GPX file."""
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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,
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"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
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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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total_dist += haversine_distance(prev[0], prev[1], p["latitude"], p["longitude"])
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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:
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uphill += diff
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else:
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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 = "running"
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if track.type:
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sport = track.type.lower()
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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,
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"start_time": start_time_str,
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"distance_m": total_dist,
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"duration_s": duration,
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"elevation_gain_m": uphill,
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"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,
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"avg_power": None,
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"normalized_power": None,
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"avg_speed_ms": (total_dist / duration) if (total_dist and duration) else None,
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"max_speed_ms": None,
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"avg_temperature_c": None,
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"calories": None,
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"training_stress_score": None,
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"vo2max_estimate": None,
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"polyline": encoded_polyline,
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"bounding_box": bounding_box,
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"source_type": "gpx",
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"data_points": data_points,
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"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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"""Calculate % time in each HR zone using user's configured max HR."""
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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 {} |