Fix wellness_parser - had fit_parser content instead of wellness parser
This commit is contained in:
@@ -1,284 +1,257 @@
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"""
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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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Garmin wellness FIT file parser using the official Garmin FIT Python SDK.
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SDK field names 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 datetime import datetime, timezone, date
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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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FIT_EPOCH_S = 631065600
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def _safe_float(val) -> Optional[float]:
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def _fit_ts(raw) -> Optional[datetime]:
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if raw is None:
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return None
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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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s = int(raw)
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if s <= 0 or s == 0xFFFFFFFF:
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return None
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return datetime.fromtimestamp(s + FIT_EPOCH_S, tz=timezone.utc)
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except (TypeError, ValueError, OverflowError, OSError):
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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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def _to_date(val) -> Optional[date]:
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if val is None:
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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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if isinstance(val, datetime):
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if val.tzinfo is None:
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val = val.replace(tzinfo=timezone.utc)
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return val.date()
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if isinstance(val, (int, float)):
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dt = _fit_ts(val)
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return dt.date() if dt else None
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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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def parse_wellness_fit(file_path: str) -> dict:
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"""
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Parse a Garmin wellness/monitoring FIT file.
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Returns {"days": {date: metrics_dict}, "error": str|None}
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"""
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daily = {}
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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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def ensure_day(d: date) -> dict:
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if d not in daily:
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daily[d] = {
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"heart_rates": [],
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"stress_values": [],
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"spo2_readings": [],
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"sleep_levels": [],
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"steps": None,
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"floors_climbed": None,
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"active_calories": None,
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"total_calories": None,
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"resting_hr": None,
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"hrv_nightly_avg": None,
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"hrv_5min_high": None,
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"hrv_status": None,
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}
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return daily[d]
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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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def listener(mesg_num: int, msg: dict):
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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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# monitoring_info (147)
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if mesg_num == 147:
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d = _to_date(msg.get("timestamp") or msg.get("localTimestamp"))
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rhr = msg.get("restingHeartRate")
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if d and rhr and 20 < rhr < 120:
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ensure_day(d)["resting_hr"] = int(rhr)
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start_time = _ensure_utc(session.get("startTime"))
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# monitoring (148)
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elif mesg_num == 148:
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d = _to_date(msg.get("timestamp") or msg.get("localTimestamp"))
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if not d:
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return
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entry = ensure_day(d)
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hr = msg.get("heartRate")
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if hr and 20 < hr < 250:
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entry["heart_rates"].append(int(hr))
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steps = msg.get("steps") or msg.get("cycles")
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if steps and steps > 0:
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entry["steps"] = max(entry["steps"] or 0, int(steps))
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stress = msg.get("stressLevelValue")
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if stress is not None and stress >= 0:
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entry["stress_values"].append(int(stress))
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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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# hrv_status_summary (275)
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elif mesg_num == 275:
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d = _to_date(msg.get("timestamp"))
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if not d:
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return
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entry = ensure_day(d)
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for key in ("weeklyAverage", "lastNightAvg", "hrvNightlyAvg"):
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v = msg.get(key)
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if v and v > 0:
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entry["hrv_nightly_avg"] = float(v)
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break
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high = msg.get("lastNight5MinHigh")
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if high:
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entry["hrv_5min_high"] = float(high)
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status = msg.get("hrvStatus")
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if status:
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entry["hrv_status"] = str(status)
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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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# stress_level (132)
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elif mesg_num == 132:
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d = _to_date(msg.get("stressLevelTime") or msg.get("timestamp"))
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if not d:
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return
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stress = msg.get("stressLevelValue")
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if stress is not None and stress >= 0:
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ensure_day(d)["stress_values"].append(int(stress))
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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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# spo2_data (258)
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elif mesg_num == 258:
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d = _to_date(msg.get("timestamp"))
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if not d:
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return
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spo2 = msg.get("spo2Percent") or msg.get("readingSpo2")
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if spo2 and 50 < spo2 <= 100:
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ensure_day(d)["spo2_readings"].append(float(spo2))
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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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# sleep_level (269)
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elif mesg_num == 269:
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d = _to_date(msg.get("timestamp"))
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if not d:
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return
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level = msg.get("sleepLevel")
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if level is not None:
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if isinstance(level, str):
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level_map = {"unmeasurable": 0, "awake": 1, "light": 2, "deep": 3, "rem": 4}
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level = level_map.get(level.lower())
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if level is not None:
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ensure_day(d)["sleep_levels"].append(int(level))
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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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# Proprietary 227: per-minute stress + HR
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elif mesg_num == 227:
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ts_raw = msg.get(1) or msg.get("1")
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hr_raw = msg.get(2) or msg.get("2")
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stress_raw = msg.get(0) or msg.get("0")
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d = _to_date(ts_raw)
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if not d:
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return
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entry = ensure_day(d)
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if hr_raw and isinstance(hr_raw, (int, float)) and 20 < hr_raw < 250:
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entry["heart_rates"].append(int(hr_raw))
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if stress_raw is not None and isinstance(stress_raw, (int, float)) and stress_raw >= 0:
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entry["stress_values"].append(int(stress_raw))
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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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# Proprietary 103: daily totals
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elif mesg_num == 103:
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ts_raw = msg.get(253) or msg.get("253") or msg.get("timestamp")
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d = _to_date(ts_raw)
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if not d:
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return
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entry = ensure_day(d)
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steps = msg.get(3) or msg.get("3")
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if steps and isinstance(steps, (int, float)) and steps > 0:
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entry["steps"] = int(steps)
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floors = msg.get(4) or msg.get("4")
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if floors and isinstance(floors, (int, float)) and floors > 0:
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f = float(floors)
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entry["floors_climbed"] = round(f / 100 if f > 1000 else f, 1)
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active_cal = msg.get(5) or msg.get("5")
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if active_cal and isinstance(active_cal, (int, float)) and active_cal > 0:
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entry["active_calories"] = float(active_cal)
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total_cal = msg.get(7) or msg.get("7")
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if total_cal and isinstance(total_cal, (int, float)) and total_cal > 0:
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entry["total_calories"] = float(total_cal)
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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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# Proprietary 211: resting HR + HRV
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elif mesg_num == 211:
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ts_raw = msg.get(253) or msg.get("253") or msg.get("timestamp")
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d = _to_date(ts_raw)
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if not d:
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return
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entry = ensure_day(d)
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rhr = msg.get(0) or msg.get("0")
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if rhr and isinstance(rhr, (int, float)) and 20 < rhr < 120:
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entry["resting_hr"] = int(rhr)
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hrv = msg.get(1) or msg.get("1")
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if hrv and isinstance(hrv, (int, float)) and 5 < hrv < 300:
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entry["hrv_nightly_avg"] = float(hrv)
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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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# Proprietary 55: activity accumulations
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elif mesg_num == 55:
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ts_raw = msg.get(253) or msg.get("253") or msg.get("timestamp")
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d = _to_date(ts_raw)
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if not d:
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return
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entry = ensure_day(d)
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steps = msg.get(2) or msg.get("2")
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if steps and isinstance(steps, (int, float)) and steps > 0:
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entry["steps"] = max(entry["steps"] or 0, int(steps))
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hr = msg.get(19) or msg.get("19")
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if hr and isinstance(hr, (int, float)) and 20 < hr < 250:
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entry["heart_rates"].append(int(hr))
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try:
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stream = Stream.from_file(file_path)
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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=False,
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mesg_listener=listener,
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)
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except Exception as e:
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return {"error": str(e), "days": {}}
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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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result = {}
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for day_date, data in daily.items():
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hrs = data.pop("heart_rates", [])
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stresses = data.pop("stress_values", [])
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spo2s = data.pop("spo2_readings", [])
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sleep_levels = data.pop("sleep_levels", [])
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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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avg_hr = round(sum(hrs) / len(hrs), 1) if hrs else None
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max_hr = max(hrs) if hrs else None
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avg_stress = round(sum(s for s in stresses if s >= 0) / len(stresses), 1) if stresses else None
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spo2_avg = round(sum(spo2s) / len(spo2s), 1) if spo2s else None
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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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if sleep_levels:
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sleep_deep_s = sum(30 for l in sleep_levels if l == 3) or None
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sleep_light_s = sum(30 for l in sleep_levels if l == 2) or None
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sleep_rem_s = sum(30 for l in sleep_levels if l == 4) or None
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sleep_awake_s = sum(30 for l in sleep_levels if l == 1) or None
|
||||
sleep_duration_s = (sleep_deep_s or 0) + (sleep_light_s or 0) + (sleep_rem_s or 0) or None
|
||||
else:
|
||||
downhill += abs(diff)
|
||||
sleep_deep_s = sleep_light_s = sleep_rem_s = sleep_awake_s = sleep_duration_s = None
|
||||
|
||||
hrs = [p["heart_rate"] for p in data_points if p["heart_rate"]]
|
||||
start_time_str = data_points[0]["timestamp"] if data_points else None
|
||||
start_dt = datetime.fromisoformat(start_time_str) if start_time_str else None
|
||||
end_dt = datetime.fromisoformat(data_points[-1]["timestamp"]) if data_points else None
|
||||
duration = (end_dt - start_dt).total_seconds() if (start_dt and end_dt) else None
|
||||
result[day_date] = {
|
||||
"resting_hr": data.get("resting_hr"),
|
||||
"avg_hr_day": avg_hr,
|
||||
"max_hr_day": max_hr,
|
||||
"avg_stress": avg_stress,
|
||||
"spo2_avg": spo2_avg,
|
||||
"hrv_nightly_avg": data.get("hrv_nightly_avg"),
|
||||
"hrv_5min_high": data.get("hrv_5min_high"),
|
||||
"hrv_status": data.get("hrv_status"),
|
||||
"steps": data.get("steps"),
|
||||
"floors_climbed": data.get("floors_climbed"),
|
||||
"active_calories": data.get("active_calories"),
|
||||
"total_calories": data.get("total_calories"),
|
||||
"sleep_duration_s": sleep_duration_s,
|
||||
"sleep_deep_s": sleep_deep_s,
|
||||
"sleep_light_s": sleep_light_s,
|
||||
"sleep_rem_s": sleep_rem_s,
|
||||
"sleep_awake_s": sleep_awake_s,
|
||||
}
|
||||
|
||||
sport = "running"
|
||||
if track.type:
|
||||
sport = track.type.lower()
|
||||
|
||||
return {
|
||||
"name": track.name or gpx.name or f"Activity {start_dt.date() if start_dt else ''}",
|
||||
"sport_type": sport,
|
||||
"start_time": start_time_str,
|
||||
"distance_m": total_dist,
|
||||
"duration_s": duration,
|
||||
"elevation_gain_m": uphill,
|
||||
"elevation_loss_m": downhill,
|
||||
"avg_heart_rate": (sum(hrs) / len(hrs)) if hrs else None,
|
||||
"max_heart_rate": max(hrs) if hrs else None,
|
||||
"avg_cadence": None,
|
||||
"avg_power": None,
|
||||
"normalized_power": None,
|
||||
"avg_speed_ms": (total_dist / duration) if (total_dist and duration) else None,
|
||||
"max_speed_ms": None,
|
||||
"avg_temperature_c": None,
|
||||
"calories": None,
|
||||
"training_stress_score": None,
|
||||
"vo2max_estimate": None,
|
||||
"polyline": encoded_polyline,
|
||||
"bounding_box": bounding_box,
|
||||
"source_type": "gpx",
|
||||
"data_points": data_points,
|
||||
"laps": [],
|
||||
}
|
||||
|
||||
|
||||
def calculate_hr_zones(data_points: list, user_max_hr: float) -> dict:
|
||||
"""Calculate % time in each HR zone using user's configured max HR."""
|
||||
if not user_max_hr or user_max_hr < 100:
|
||||
return {}
|
||||
|
||||
zone_bounds = [0.0, 0.60, 0.70, 0.80, 0.90, 1.01]
|
||||
zone_keys = ["z1", "z2", "z3", "z4", "z5"]
|
||||
zones = {k: 0 for k in zone_keys}
|
||||
total = 0
|
||||
|
||||
for p in data_points:
|
||||
hr = p.get("heart_rate")
|
||||
if not hr or hr < 20:
|
||||
continue
|
||||
pct = hr / user_max_hr
|
||||
total += 1
|
||||
for i, key in enumerate(zone_keys):
|
||||
if zone_bounds[i] <= pct < zone_bounds[i+1]:
|
||||
zones[key] += 1
|
||||
break
|
||||
else:
|
||||
zones["z5"] += 1
|
||||
|
||||
if total:
|
||||
return {k: round(v / total * 100, 1) for k, v in zones.items()}
|
||||
return {}
|
||||
return {"days": result, "error": None}
|
||||
Reference in New Issue
Block a user